Recent advances in 3D Gaussian Splatting (3DGS) have focused on accelerating optimization while preserving reconstruction quality. However, many proposed methods entangle implementation-level improvements with fundamental algorithmic modifications or trade performance for fidelity, leading to a fragmented research landscape that complicates fair comparison. In this work, we consolidate and evaluate the most effective and broadly applicable strategies from prior 3DGS research and augment them with several novel optimizations. We further investigate underexplored aspects of the framework, including numerical stability, Gaussian truncation, and gradient approximation. The resulting system, Faster-GS, provides a rigorously optimized algorithm that we evaluate across a comprehensive suite of benchmarks. Our experiments demonstrate that Faster-GS achieves up to 5$\times$ faster training while maintaining visual quality, establishing a new cost-effective and resource efficient baseline for 3DGS optimization. Furthermore, we demonstrate that optimizations can be applied to 4D Gaussian reconstruction, leading to efficient non-rigid scene optimization.

翻译：近期，3D高斯泼溅（3DGS）领域的进展主要聚焦于在保持重建质量的同时加速优化过程。然而，许多已提出的方法将实现层面的改进与基础算法修改相混淆，或以性能换取保真度，导致研究格局碎片化，使得公平比较变得复杂。在本工作中，我们整合并评估了先前3DGS研究中最有效且广泛适用的策略，并辅以若干新颖的优化方法。我们进一步探究了该框架中尚未被充分探索的方面，包括数值稳定性、高斯截断和梯度近似。由此产生的系统——Faster-GS——提供了一种经过严格优化的算法，我们在全面的基准测试套件中对其进行了评估。实验表明，Faster-GS在保持视觉质量的同时，实现了高达5倍的训练加速，为3DGS优化建立了一个新的高性价比且资源高效的基准。此外，我们证明了这些优化方法可应用于4D高斯重建，从而实现高效的非刚性场景优化。